BEVNav：基于鸟瞰图时空对比学习的机器人自主导航
BEVNav: Robot Autonomous Navigation Via Spatial-Temporal Contrastive Learning in Bird's-Eye View
AI总结：
本文提出BEVNav方法，通过自监督时空对比学习提取鸟瞰图表示，并结合Soft Actor-Critic强化学习，提升机器人在无地图密集行人环境中的自主导航性能。
摘要：
在无地图环境中，目标驱动的移动机器人导航需要有效的状态表示以实现可靠决策。受鸟瞰图（BEV）在点云视觉感知中良好特性的启发，本文提出了一种名为BEVNav的新型导航方法。该方法采用深度强化学习来学习BEV表示并提升决策可靠性。首先，我们提出了一种自监督时空对比学习方法用于学习BEV表示。在空间上，来自点云的两个随机增强视图相互预测，以增强空间特征。在时间上，我们将当前观测与连续帧的动作相结合来预测未来特征，从而建立观测转移与动作之间的关系以捕获时间线索。随后，将这种时空对比学习融入Soft Actor-Critic强化学习框架中，我们的BEVNav提供了更优的导航策略。大量实验表明，BEVNav在行人密集的环境中具有鲁棒性，并在多个基准测试中优于最先进的方法。代码将在https://github.com/LanrenzzzZ/BEVNav公开发布。
英文摘要：
Goal-driven mobile robot navigation in map-less environments requires effective state representations for reliable decision-making. Inspired by the favorable properties of Bird's-Eye View (BEV) in point clouds for visual perception, this paper introduces a novel navigation approach named BEVNav. It employs deep reinforcement learning to learn BEV representations and enhance decision-making reliability. First, we propose a self-supervised spatial-temporal contrastive learning approach to learn BEV representations. Spatially, two randomly augmented views from a point cloud predict each other, enhancing spatial features. Temporally, we combine the current observation with consecutive frames' actions to predict future features, establishing the relationship between observation transitions and actions to capture temporal cues. Then, incorporating this spatial-temporal contrastive learning in the Soft Actor-Critic reinforcement learning framework, our BEVNav offers a superior navigation policy. Extensive experiments demonstrate BEVNav's robustness in environments with dense pedestrians, outperforming state-of-the-art methods across multiple benchmarks. \rev{The code will be made publicly available at https://github.com/LanrenzzzZ/BEVNav.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
